High Infection Rate Outcomes in Long-bone Tumor Surgery with Endoprosthetic Reconstruction in Adults: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Limb salvage surgery (LSS) with endoprosthetic replacement is the most common method of reconstruction following bone tumor resection in the adult population. The risk of a postoperative infection developing is high when compared with conventional arthroplasty and there are no appropriate guidelines for antibiotic prophylaxis. QUESTIONS/PURPOSES: We sought to answer the following questions: (1) What is the overall risk of deep infection and the causative organism in lower-extremity long-bone tumor surgery with endoprosthetic reconstruction? (2) What antibiotic regimens are used with endoprosthetic reconstruction? (3) Is there a correlation between infection and either duration of postoperative antibiotics or sample size? METHODS: We conducted a systematic review of the literature for clinical studies that reported infection rates in adults with primary bony malignancies of the lower extremity treated with surgery and endoprosthetic reconstruction. The search included articles published in English between 1980 and July 2011. RESULTS: The systematic literature review yielded 48 studies reporting on a total of 4838 patients. The overall pooled weighted infection rate for lower-extremity LSS with endoprosthetic reconstruction was approximately 10% (95% CI, 8%-11%), with the most common causative organism reported to be Gram-positive bacteria in the majority of cases. The pooled weighted infection rate was 13% after short-term postoperative antibiotics and 8% after long-term postoperative antibiotics. There was no correlation between sample size and infection rate. CONCLUSIONS: Infection rates of 10% are high when compared with rates for conventional arthroplasty. Our results suggest that long-term antibiotic prophylaxis decreases the risk of deep infection. However, the data should be interpreted with caution owing to the retrospective nature of the studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".